# Neural Network Models for Stock Selection Based on Fundamental Analysis

arXiv: https://arxiv.org/abs/1906.05327 · HTML: https://mlsanity.com/paper/1906.05327/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [ETF Portfolio Construction via Neural Network trained on Financial Statement Data](https://mlsanity.com/paper/2207.01187/) (2022)

## Abstract (arXiv)

Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial ratios. The study is designed to evaluate the performance of each architecture based on the relative return of the selected portfolios with respect to the benchmark stock index. The results show that both architectures possess the ability to separate winners and losers from a sample universe of stocks, and the selected portfolios outperform the benchmark. Our study argues that FNN shows superior performance over ANFIS.